There is a diagram most security teams still carry in their heads when they think about AI. A user talks to a chatbot. The chatbot talks to a large language model. The model talks back. Put an inspection point in the middle, and the problem is solved.

That diagram was accurate for about eighteen months. It is not accurate anymore.

Look inside a modern AI application and the chatbot is the least interesting thing in it. There are agents living inside the application, making decisions and calling functions without a human typing anything. There are MCP servers sitting outside the application, connecting models to real systems and live data. There are tools (scripts, APIs, plugins) that the model can invoke, retrieved documents flowing into context windows, and orchestration layers deciding which agent hands work to which. The application is no longer app-plus-LLM. It is an ecosystem.

What is agentic AI security?

Agentic AI security is the practice of securing AI applications that take actions, not just applications that answer questions. It covers the whole AI execution path: the agents making decisions, the tools and APIs they invoke, the MCP servers connecting them to live systems, the documents retrieved into context, and the orchestration layer routing work between agents. Prompt inspection alone covers only the first hop.

That matters for one uncomfortable reason: security architectures inherit their assumptions from the diagrams they were designed against. If your AI protection assumes a single conversation crossing a single boundary, it is defending an architecture nobody runs anymore.

What changed in the AI attack surface

Three things, in quick succession.

First, agents started taking actions. A chatbot that gives a wrong answer is embarrassing. An agent that acts on a manipulated instruction is an operational incident. It can query a database, call an API, or trigger a workflow before any human notices. The blast radius of a successful prompt injection grew from “bad reply” to “bad transaction.”

Second, the Model Context Protocol and similar tool-integration patterns arrived. MCP is useful: a standard bridge between AI assistants and real systems. It is also, by definition, an expansion of the attack surface. Every tool a model can reach is something an attacker can try to reach through the model. We unpack those exposures in detail in MCP Server Security: The Blind Spot in Your AI Stack (https://www.imperva.com/blog/mcp-server-security-blind-spot-ai-stack/).

Third, the interactions multiplied. It is no longer one train rolling through one tunnel, one prompt in and one response out. It is internal communication between sub-agents, external calls to MCP servers, retrieval from knowledge stores: traffic that never touches the chat window at all.

The honest question: would you know what your agents just did?

The challenge isn’t simply preventing prompt injection attacks anymore. It’s understanding everything the application is capable of doing.

So here is the question worth asking your team this week: if an agent inside one of your applications called a tool it had never called before, would anyone know?

For most organizations, the honest answer is no. Not because the security team is behind, but because the visibility layer for this architecture is only now being built. You cannot govern what you cannot see, and most of what modern AI applications do is currently unseen.

That is where AI application security is heading as a discipline: away from inspecting a single conversation, toward securing the entire AI execution path: discovering every AI asset in the environment, understanding how agents, models, and tools actually relate to each other, and enforcing policy on the interactions that matter.

The chatbot era gave us a clean diagram and a comforting inspection point. The agent era gives us an ecosystem. Security is moving with it. The teams that update their mental diagram first will be the ones that are not surprised later.

Frequently asked questions about agentic AI security

What is the difference between LLM security and agentic AI security?

LLM security protects the conversation: what goes into the model and what comes back out. Agentic AI security protects what happens next – the tools the agent calls, the systems it reaches through MCP, the data it retrieves, and the handoffs between agents. The blast radius of a successful attack grows from a bad reply to a bad transaction.

Why isn’t prompt inspection enough for AI agents?

Because most agentic traffic never touches the chat window. Sub-agent communication, MCP tool calls and retrieval from knowledge stores all happen behind the prompt. An inspection point placed on the user conversation sees none of it.

What are the main agentic AI security risks?

The OWASP Top 10 for Agentic Applications (2026) names agent goal hijack, tool misuse and exploitation, identity and privilege abuse, agentic supply chain vulnerabilities, unexpected code execution, memory and context poisoning, insecure inter-agent communication, cascading failures, human-agent trust exploitation, and rogue agents.

How do you get visibility into what AI agents are doing?

Start with discovery: inventory every AI asset in the environment – models, agents, tools and MCP servers – then map how they relate to each other, then enforce policy on the interactions that matter. You cannot govern what you cannot see.

Does Imperva secure AI agents and MCP servers?

Imperva AI Application Security protects LLM-backed applications today. Thales announced the AI Security Fabric in December 2025, with an MCP security gateway and end-to-end runtime access control for agentic AI interactions on the 2026 roadmap.

Thales’s Imperva AI Application Security protects the AI powering your business applications, from LLM-backed chatbots to agents, RAG pipelines, and MCP-connected tools. Thales announced the AI Security Fabric in December 2025, adding an MCP security gateway and runtime access control for agentic AI interactions to the 2026 roadmap. See how it works here or read more in our white paper: Beyond the LLM Top 10 (Coming Soon)